title: Robust and sensitive video motion detection for sleep analysis.
abstract: In this paper, we propose a camera-based system combining video motion detection, motion estimation, and texture analysis with machine learning for sleep analysis. The system is robust to time-varying illumination conditions while using standard camera and infrared illumination hardware. We tested the system for periodic limb movement (PLM) detection during sleep, using EMG signals as a reference. We evaluated the motion detection performance both per frame and with respect to movement event classification relevant for PLM detection. The Matthews correlation coefficient improved by a factor of 2, compared to a state-of-the-art motion detection method, while sensitivity and specificity increased with 45% and 15%, respectively. Movement event classification improved by a factor of 6 and 3 in constant and highly varying lighting conditions, respectively. On 11 PLM patient test sequences, the proposed system achieved a 100% accurate PLM index (PLMI) score with a slight temporal misalignment of the starting time (<1 s) regarding one movement. We conclude that camera-based PLM detection during sleep is feasible and can give an indication of the PLMI score.
authors.forename: Adrienne
authors.initials: A
authors.lastname: Heinrich
authors.forename: Di
authors.initials: D
authors.lastname: Geng
authors.forename: Dmitry
authors.initials: D
authors.lastname: Znamenskiy
authors.forename: Jelte Peter
authors.initials: JP
authors.lastname: Vink
authors.forename: Gerard
authors.initials: G
authors.lastname: de Haan
mesh_terms: D004576:Electromyography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007866:Leg / Q000502:physiology; D009068:Movement / Q000502:physiology*; D017286:Polysomnography / Q000379:methods*; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*